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Sökning: WFRF:(Yaseen Zaher Mundher)

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1.
  • Afan, Haitham Abdulmohsin, et al. (författare)
  • Thermal and Hydraulic Performances of Carbon and Metallic Oxides-Based Nanomaterials
  • 2022
  • Ingår i: Nanomaterials. - : MDPI AG. - 2079-4991. ; 12:9
  • Tidskriftsartikel (refereegranskat)abstract
    • For companies, notably in the realms of energy and power supply, the essential requirement for highly efficient thermal transport solutions has become a serious concern. Current research highlighted the use of metallic oxides and carbon-based nanofluids as heat transfer fluids. This work examined two carbon forms (PEG@GNPs & PEG@TGr) and two types of metallic oxides (Al2O3 & SiO2) in a square heated pipe in the mass fraction of 0.1 wt.%. Laboratory conditions were as follows: 6401 ≤ Re ≤ 11,907 and wall heat flux = 11,205 W/m2. The effective thermal–physical and heat transfer properties were assessed for fully developed turbulent fluid flow at 20–60 °C. The thermal and hydraulic performances of nanofluids were rated in terms of pumping power, performance index (PI), and performance evaluation criteria (PEC). The heat transfer coefficients of the nanofluids improved the most: PEG@GNPs = 44.4%, PEG@TGr = 41.2%, Al2O3 = 22.5%, and SiO2 = 24%. Meanwhile, the highest augmentation in the Nu of the nanofluids was as follows: PEG@GNPs = 35%, PEG@TGr = 30.1%, Al2O3 = 20.6%, and SiO2 = 21.9%. The pressure loss and friction factor increased the highest, by 20.8–23.7% and 3.57–3.85%, respectively. In the end, the general performance of nanofluids has shown that they would be a good alternative to the traditional working fluids in heat transfer requests.
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2.
  • Al-Janabi, Ahmed Mohammed Sami, et al. (författare)
  • Experimental and Numerical Analysis for Earth-Fill Dam Seepage
  • 2020
  • Ingår i: Sustainability. - Switzerland : MDPI. - 2071-1050. ; 12:6, s. 1-14
  • Tidskriftsartikel (refereegranskat)abstract
    • Earth-fill dams are the most common types of dam and the most economical choice. However, they are more vulnerable to internal erosion and piping due to seepage problems that are the main causes of dam failure. In this study, the seepage through earth-fill dams was investigated using physical, mathematical, and numerical models. Results from the three methods revealed that both mathematical calculations using L. Casagrande solutions and the SEEP /Wnumerical model have a plotted seepage line compatible with the observed seepage line in the physical model. However,when the seepage flow intersected the downstream slope and when piping took place, the use of SEEP /Wto calculate the flow rate became useless as it was unable to calculate the volume of water flow in pipes. This was revealed by the big dierence in results between physical and numerical models in the first physical model, while the results were compatible in the second physical model when the seepage line stayed within the body of the dam and low compacted soil was adopted. Seepage analysis for seven dierent configurations of an earth-fill dam was conducted using the SEEP /W model at normal and maximum water levels to find the most appropriate configuration among them. The seven dam configurations consisted of four homogenous dams and three zoned dams. Seepage analysis revealed that if sucient quantity of silty sand soil is available around the proposed dam location, a homogenous earth-fill dam with a medium drain length of 0.5 m thickness is the best design configuration. Otherwise, a zoned earth-fill dam with a central core and 1:0.5 Horizontal to Vertical ratio (H:V) is preferred.
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3.
  • Al-Janabi, Ahmed Mohammed Sami, et al. (författare)
  • Optimizing Height and Spacing of Check Dam Systems for Better Grassed Channel Infiltration Capacity
  • 2020
  • Ingår i: Applied Sciences. - Switzerland : MDPI. - 2076-3417. ; 10:11
  • Tidskriftsartikel (refereegranskat)abstract
    • The check dams in grassed stormwater channels enhance infiltration capacity by temporarily blocking water flow. However, the design properties of check dams, such as their height and spacing, have a significant influence on the flow regime in grassed stormwater channels and thus channel infiltration capacity. In this study, a mass-balance method was applied to a grassed channel model to investigate the effects of height and spacing of check dams on channel infiltration capacity. Moreover, an empirical infiltration model was derived by improving the modified Kostiakov model for reliable estimation of infiltration capacity of a grassed stormwater channel due to check dams from four hydraulic parameters of channels, namely, the water level, channel base width, channel side slope, and flow velocity. The result revealed that channel infiltration was increased from 12% to 20% with the increase of check dam height from 10 to 20 cm. However, the infiltration was found to decrease from 20% to 19% when a 20 cm height check dam spacing was increased from 10 to 30 m. These results indicate the effectiveness of increasing height of check dams for maximizing the infiltration capacity of grassed stormwater channels and reduction of runoff volume.
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4.
  • Al-Sulttani, Ali Omran, et al. (författare)
  • Thermal effectiveness of solar collector using Graphene nanostructures suspended in ethylene glycol–water mixtures
  • 2022
  • Ingår i: Energy Reports. - : Elsevier BV. - 2352-4847. ; 8, s. 1867-1882
  • Tidskriftsartikel (refereegranskat)abstract
    • Flat plate solar collectors (FPSCs) are the most often used as solar collectors due to their easiness of installation and usage. The current research investigates the energy efficiency of FPSC using different mass concentration with varied base fluids containing Graphene nanofluids (T-Gr). Mass concentration of 0.1%-wt., 0.075%-wt., 0.050%-wt. and 0.025%-wt. were mixed with ethylene glycol (EG) and distilled water (DW) in different rations. The operating conditions were volumetric flowrate (1.5, 1 and 0.5) LPM 50 °C-input fluid temperature and 800 W/m2-global solar irradiation. Scanning electron microscope (SEM) and energy dispersive X-ray (EDX) were used to synthesize the thermally treated nanomaterial. The theoretical investigation indicated that using T-Gr nanosuspensions increased the FPSC efficiency in comparison with the host fluid for all examined mass concentrations and volumetric flowrates. In quantitative terms, the maximum thermal effectiveness improvement for the EG, (DW:70 + EG:30) and DW:EG (DW:50 + EG:50) and using flowrates of (1.5, 1 and 0.5) LPM were 12.54%, 12.46% and 12.48%. In addition, the research results pointed that the essential parameters (i.e., loss energy (FRUL)) and gain energy (FR (τα)) of the T-Gr nanofluids were increased significantly.
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5.
  • Alawi, Omer A., et al. (författare)
  • Heat transfer and hydrodynamic properties using different metal-oxide nanostructures in horizontal concentric annular tube : An optimization study
  • 2021
  • Ingår i: Nanomaterials. - : MDPI AG. - 2079-4991. ; 11:8
  • Tidskriftsartikel (refereegranskat)abstract
    • Numerical studies were performed to estimate the heat transfer and hydrodynamic properties of a forced convection turbulent flow using three-dimensional horizontal concentric annuli. This paper applied the standard k–ε turbulence model for the flow range 1 × 104 ≤ Re ≥ 24 × 103. A wide range of parameters like different nanomaterials (Al2O3, CuO, SiO2 and ZnO), different particle nanoshapes (spherical, cylindrical, blades, platelets and bricks), different heat flux ratio (HFR) (0, 0.5, 1 and 2) and different aspect ratios (AR) (1.5, 2, 2.5 and 3) were examined. Also, the effect of inner cylinder rotation was discussed. An experiment was conducted out using a field-emission scanning electron microscope (FE-SEM) to characterize metallic oxides in spherical morphologies. Nano-platelet particles showed the best enhancements in heat transfer properties, followed by nano-cylinders, nano-bricks, nano-blades, and nano-spheres. The maximum heat transfer enhancement was found in SiO2, followed by ZnO, CuO, and Al2O3, in that order. Meanwhile, the effect of the HFR parameter was insignificant. At Re = 24,000, the inner wall rotation enhanced the heat transfer about 47.94%, 43.03%, 42.06% and 39.79% for SiO2, ZnO, CuO and Al2O3, respectively. Moreover, the AR of 2.5 presented the higher heat transfer improvement followed by 3, 2, and 1.5.
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6.
  • Alawi, Omer A., et al. (författare)
  • Thermohydraulic performance of thermal system integrated with twisted turbulator inserts using ternary hybrid nanofluids
  • 2023
  • Ingår i: Nanotechnology Reviews. - : Walter de Gruyter. - 2191-9089 .- 2191-9097. ; 12:1
  • Tidskriftsartikel (refereegranskat)abstract
    • Mono, hybrid, and ternary nanofluids were tested inside the plain and twisted-tape pipes using k-omega shear stress transport turbulence models. The Reynolds number was 5,000 ≤ Re ≤ 15,000, and thermophysical properties were calculated under the condition of 303 K. Single nanofluids (Al2O3/distilled water [DW], SiO2/DW, and ZnO/DW), hybrid nanofluids (SiO2 + Al2O3/DW, SiO2 + ZnO/DW, and ZnO + Al2O3/DW) in the mixture ratio of 80:20, and ternary nanofluids (SiO2 + Al2O3 + ZnO/DW) in the mixture ratio of 60:20:20 were estimated in different volumetric concentrations (1, 2, 3, and 4%). The twisted pipe had a higher outlet temperature than the plain pipe, while SiO2/DW had a lower Tout value with 310.933 K (plain pipe) and 313.842 K (twisted pipe) at Re = 9,000. The thermal system gained better energy using ZnO/DW with 6178.060 W (plain pipe) and 8426.474 W (twisted pipe). Furthermore, using SiO2/DW at Re = 9,000, heat transfer improved by 18.017% (plain pipe) and 21.007% (twisted pipe). At Re = 900, the pressure in plain and twisted pipes employing SiO2/DW reduced by 167.114 and 166.994%, respectively. In general, the thermohydraulic performance of DW and nanofluids was superior to one. Meanwhile, with Re = 15,000, DW had a higher value of η Thermohydraulic = 1.678
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7.
  • Ameen, Ameen Mohammed Salih, et al. (författare)
  • Minimizing the Principle Stresses of Powerhoused Rock-Fill Dams Using Control Turbine Running Units: Application of Finite Element Method
  • 2018
  • Ingår i: Water. - : MDPI. - 2073-4441. ; 10:9
  • Tidskriftsartikel (refereegranskat)abstract
    • This study focuses on improving the safety of embankment dams by considering theeffects of vibration due to powerhouse operation on the dam body. The study contains two ainparts. In the first part, ANSYS-CFX is used to create the three-dimensional (3D) Finite Volume (FV)model of one vertical Francis turbine unit. The 3D model is run by considering various reservoirconditions and the dimensions of units. The Re-Normalization Group (RNG) k-?? turbulence modelis employed, and the physical properties of water and the flow haracteristics are defined in theturbine model. In the second phases, a 3D finite element (FE) numerical model for a rock-fill dam iscreated by using ANSYS®, considering the dam connection with its powerhouse represented by fourvertical Francis turbines, foundation, and the upstream reservoir. Changing the upstream watertable minimum and maximum water levels, standers earth gravity, fluid-solid interface, hydrostaticpressure, and the soil properties are onsidered. The dam model runs to cover all possibilities forturbines operating in accordance with the reservoir discharge ranges. In order to minimize stressesin the dam body and increase dam safety, this study optimizes the turbine operating system byintegrating turbine and dam models.
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8.
  • Arafa, Salaheddin, et al. (författare)
  • Investigation into the permeability and strength of pervious geopolymer concrete containing coated biomass aggregate material
  • 2021
  • Ingår i: Journal of Materials Research and Technology. - : Elsevier. - 2238-7854 .- 2214-0697. ; 15, s. 2075-2087
  • Tidskriftsartikel (refereegranskat)abstract
    • Waste palm oil products can be recycled in the production of pervious geopolymer concrete (PGC) for long-term sustainable development. PGC is a non-slip porous pavement concrete that allows water to pass through. Biomass aggregate (BA) is produced by burning palm oil biomass and is introduced as a replacement for natural aggregate (NA). BA is mixed with fly ash (FA) and alkaline liquid (AL) and heated in an oven at 80 °C for 24 h to produce coated biomass aggregate (CBA). PGC containing CBA is commonly used as a cement substitute in concrete. This study aims to develop and evaluate the effect of rainfall intensity on the ability of PGC to reduce stormwater runoff. Coating BA with geopolymer paste resulted in improved properties, better Aggregate crushing value (ACV), Aggregate impact value (AIV), water absorption and higher compressive strength compared with BA. Results indicated, a PGC with a FA/CBA ratio of 1:7, CBA of 5–10 mm, NaOH molarity of 10 M, Na2SiO3/NaOH ratio of 2.5, and AL/FA ratio of 0.5 when cured in an oven for 24 h at 80 °C, gave the optimum ratio for compressive strength of 13.7 MPa and water permeability of 2.1 cm/s. Both BA and CBA revealed a viable alternative aggregates for producing PGC and that the compressive strength of PGC made with CBA was 51% greater than cement pervious concrete containing NA. The results also showed that the reduction in runoff was due to the permeable concrete and decreased runoff with the increased rainfall intensity.
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9.
  • Armanuos, Asaad M., et al. (författare)
  • Assessing the Effectiveness of Using Recharge Wells for Controlling the Saltwater Intrusion in Unconfined Coastal Aquifers with Sloping Beds : Numerical Study
  • 2020
  • Ingår i: Sustainability. - Switzerland : MDPI. - 2071-1050. ; 12:7
  • Tidskriftsartikel (refereegranskat)abstract
    • Groundwater systems are considered major freshwater sources for many coastal aquifers worldwide. Seawater intrusion (SWI) inland into freshwater coastal aquifers is a common environmental problem that causes deterioration of the groundwater quality. This research investigates the effectiveness of using an injection through a well to mitigate the SWI in sloping beds of unconfined coastal aquifers. The interface was simulated using SEAWAT code. The repulsion ratios due to the length of the SWI wedge (RL) and the area of the saltwater wedge (RA) were computed. A sensitivity analysis was conducted to recognize the change in the confining layer bed slope (horizontal, positive, and negative) and hydraulic parameters of the value of the SWI repulsion ratio. Injection at the toe itself achieved higher repulsion ratios. RL and RA declined if the injection point was located remotely and higher than the toe of the seawater wedge. Installation at the toe achieved a higher RL in positive sloping followed by horizontal and negative slopes. Moreover, the highest value of RA could be reached by injecting at the toe itself with a horizontal bed aquifer, followed by negative and positive slopes. The recharge well is confirmed as one of the most effective applications for the mitigation of SWI in sloping bed aquifers. The Akrotiri case study shows that the proposed recharging water method has a significant impact on controlling SWI and declines in both SWI wedge length and area.
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10.
  • Armanuos, Asaad M., et al. (författare)
  • Cross Assessment of Twenty-One Different Methods for Missing Precipitation Data Estimation
  • 2020
  • Ingår i: Atmosphere. - Switzerland : MDPI. - 2073-4433. ; 11:4, s. 1-35
  • Tidskriftsartikel (refereegranskat)abstract
    • The  results  of  metrological,  hydrological,  and  environmental  data  analyses  are  mainlydependent  on  the  reliable  estimation  of  missing  data.  In  this  study,  21  classical  methods  were evaluated to determine the best method for infilling the missing precipitation data in Ethiopia. The monthly data collected from 15 different stations over 34 years from 1980 to 2013 were considered. Homogeneity  and  trend  tests  were  performed  to  check  the  data.  The  results  of  the  different methods were  compared  using the mean absolute error (MAE),  root-mean-square  error (RMSE), coefficient  of  efficiency  (CE),  similarity  index  (S-index),  skill  score  (SS),  and  Pearson  correlation coefficient (rPearson). The results of this paper confirmed that the normal ratio (NR), multiple linear regression (MLR), inverse distance weighting (IDW), correlation coefficient weighting (CCW), and arithmetic average (AA) methods are the most reliable methods of those studied. The NR method provides  the  most  accurate  estimations  with  rPearson   of  0.945,  mean  absolute  error  of  22.90  mm, RMSE of  33.695  mm,  similarity  index  of 0.999,  CE  index of  0.998,  and  skill  score of  0.998.  When comparing the observed results and the estimated results from the NR, MLR, IDW, CCW, and AA methods, the MAE and RMSE were found to be low, and high values of CE, S-index, SS, and rPearson were achieved. On the other hand, using the closet station (CS), UK traditional, linear regression (LR),  expectation  maximization  (EM),  and  multiple  imputations  (MI)  methods  gave  the  lowest accuracy, with MAE and RMSE values varying from 30.424 to 47.641 mm and from 49.564 to 58.765 mm, respectively. The results of this study suggest that the recommended methods are applicable for different types of climatic data in Ethiopia and arid regions in other countries around the world.
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11.
  • Armanuos, Asaad M., et al. (författare)
  • Underground Barrier Wall Evaluation for Controlling Saltwater Intrusion in Sloping Unconfined Coastal Aquifers
  • 2020
  • Ingår i: Water. - Switzerland : MDPI. - 2073-4441. ; 12:9
  • Tidskriftsartikel (refereegranskat)abstract
    • Barrier walls are considered one of the most effective methods for facilitating the retreat of saltwater intrusion (SWI). This research plans to examine the effect of using barrier walls for controlling of SWI in sloped unconfined aquifers. The sloping unconfined aquifer is considered with three different bed slopes. The SEAWAT model is implemented to simulate the SWI. For model validation, the numerical results of the seawater wedge at steady state were compared with the analytical solution. Increasing the ratio of flow barrier depth (db/d) forced the saltwater interface to move seaward and increased the repulsion ratio (R). With a positive sloping bed, further embedding the barrier wall from 0.2 to 0.7 caused R to increase from 0.3% to 59%, while it increased from 1.8% to 41.7% and from 3.4% to 46.9% in the case of negative and horizontal slopes, respectively. Embedding the barrier wall to a db/d value of more than 0.4 achieved a greater R value in the three bed-sloping cases. Installing the barrier wall near the saltwater side with greater depth contributed to the retreat of the SWI. With a negative bed slope, moving the barrier wall from Xb/Lo = 1.0 toward the saltwater side (Xb/Lo = 0.2) increased R from 7.21% to 68.75%, whereas R increased from 5.3% to 67% for the horizontal sloping bed and from 5.1% to 64% for the positive sloping bed. The numerical results for the Akrotiri coastal aquifer confirm that the embedment of the barrier wall significantly affects the controlling of SWI by increasing the repulsion ratio (R) and decreasing the SWI length ratio (L/La). Cost-benefit analysis is recommended to determine the optimal design of barrier walls for increasing the cost-effectiveness of the application of barrier walls as a countermeasure for controlling and preventing SWI in sloped unconfined aquifers.
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12.
  • Bhagat, Suraj Kumar, et al. (författare)
  • Evaluating Physical and Fiscal Water Leakage in Water Distribution System
  • 2019
  • Ingår i: Water. - Switzerland : MDPI. - 2073-4441. ; 11:10
  • Tidskriftsartikel (refereegranskat)abstract
    • With increasing population, the need for research ideas on the field of reducing wastage of water can save a big amount of water, money, time, and energy. Water leakage (WL) is an essential problem in the field of water supply field. This research is focused on real water loss in the water distribution system located in Ethiopia. Top-down and bursts and background estimates (BABE) methodology is performed to assess the data and the calibration process of the WL variables. The top-down method assists to quantify the water loss by the record and observation throughout the distribution network. In addition, the BABE approach gives a specific water leakage and burst information. The geometrical mean method is used to forecast the population up to 2023 along with their fiscal value by the uniform tariff method. With respect to the revenue lost, 42575 Br and 42664 Br or in 1562$ and 1566$ were lost in 2017 and 2018, respectively. The next five-year population was forecasted to estimate the possible amount of water to be saved, which was about 549,627 m3 and revenue 65,111$ to make the system more efficient. The results suggested that the majority of losses were due to several components of the distribution system including pipe-joint failure, relatively older age pipes, poor repairing and maintenance of water taps, pipe joints and shower taps, negligence of the consumer and unreliable water supply. As per the research findings, recommendations were proposed on minimizing water leakage.
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13.
  • Bokde, Neeraj Dhanraj, et al. (författare)
  • A comparison between reconstruction methods for generation of synthetic time series applied to wind speed simulation
  • 2019
  • Ingår i: IEEE Access. - UK : IEEE. - 2169-3536. ; 7, s. 1353096-135398
  • Tidskriftsartikel (refereegranskat)abstract
    • Wind energy is an attractive renewable sources and its prediction is highly essential for multiple applications. Over the literature, there are several studies have been focused on the related researches of synthetic wind speed data generation. In this research, two reconstruction methods are developed for synthetic wind speed time series generation. The modeling is constructed based on different processes including independent values generation from the known probability distribution function, rearrangement of random values and segmentation. They have been named as Rank-wise and Step-wise reconstruction methods. The proposed methods are explained with the help of a standard time series and the examination on wind speed time series collected from Galicia, the autonomous region in the northwest of Spain. Results evidenced the potential of the developed models over the state-of-the-art synthetic time series generation methods and demonstrated a successful validation using the means of mean and median wind speed values, autocorrelations, probability distribution parameters with their corresponding histograms and confusion matrix. Pros and cons of both methods are discussed comprehensively.
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14.
  • Bokde, Neeraj, et al. (författare)
  • The Hybridization of Ensemble Empirical Mode Decomposition with Forecasting Models : Application of Short-Term Wind Speed and Power Modeling
  • 2020
  • Ingår i: Energies. - Switzerland : MDPI. - 1996-1073. ; 13:7, s. 1-23
  • Tidskriftsartikel (refereegranskat)abstract
    • In this research, two hybrid intelligent models are proposed for prediction accuracy enhancement for wind speed and power modeling. The established models are based on the hybridisation of Ensemble Empirical Mode Decomposition (EEMD) with a Pattern Sequence-based Forecasting (PSF) model and the integration of EEMD-PSF with Autoregressive Integrated Moving Average (ARIMA) model. In both models (i.e., EEMD-PSF and EEMD-PSF-ARIMA), the EEMD method is used to decompose the time-series into a set of sub-series and the forecasting of each sub-series is initiated by respective prediction models. In the EEMD-PSF model, all sub-series are predicted using the PSF model, whereas in the EEMD-PSF-ARIMA model, the sub-series with high and low frequencies are predicted using PSF and ARIMA, respectively. The selection of the PSF or ARIMA models for the prediction process is dependent on the time-series characteristics of the decomposed series obtained with the EEMD method. The proposed models are examined for predicting wind speed and wind power time-series at Maharashtra state, India. In case of short-term wind power time-series prediction, both proposed methods have shown at least 18.03 and 14.78 percentage improvement in forecast accuracy in terms of root mean square error (RMSE) as compared to contemporary methods considered in this study for direct and iterated strategies, respectively. Similarly, for wind speed data, those improvement observed to be 20.00 and 23.80 percentages, respectively. These attained prediction results evidenced the potential of the proposed models for the wind speed and wind power forecasting. The current proposed methodology is transformed into R package ‘decomposedPSF’ which is discussed in the Appendix.
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15.
  • Deo, Ravinesh C., et al. (författare)
  • Modern Artificial Intelligence Model Development for Undergraduate Student Performance Prediction : An Investigation on Engineering Mathematics Courses
  • 2020
  • Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 8, s. 136697-136724
  • Tidskriftsartikel (refereegranskat)abstract
    • A computationally efficient artificial intelligence (AI) model called Extreme Learning Machines (ELM) is adopted to analyze patterns embedded in continuous assessment to model the weighted score (WS) and the examination (EX) score in engineering mathematics courses at an Australian regional university. The student performance data taken over a six-year period in multiple courses ranging from the mid- to the advanced level and a diverse course offering mode (i.e., on-campus, ONC, and online, ONL) are modelled by ELM and further benchmarked against competing models: random forest (RF) and Volterra. With the assessments and examination marks as key predictors of WS (leading to a grade in the mid-level course), ELM (with respect to RF and Volterra) outperformed its counterpart models both for the ONC and the ONL offer. This generated relative prediction error in the testing phase, of only 0.74%, compared to about 3.12% and 1.06%, respectively, while for the ONL offer, the prediction errors were only 0.51% compared to about 3.05% and 0.70%. In modelling the student performance in advanced engineering mathematics course, ELM registered slightly larger errors: 0.77% (vs. 22.23% and 1.87%) for ONC and 0.54% (vs. 4.08% and 1.31%) for the ONL offer. This study advocates a pioneer implementation of a robust AI methodology to uncover relationships among student learning variables, developing teaching and learning intervention and course health checks to address issues related to graduate outcomes, and student learning attributes in the higher education sector.
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16.
  • Ehteram, Mohammad, et al. (författare)
  • Hybridization of artificial intelligence models with nature inspired optimization algorithms for lake water level prediction and uncertainty analysis
  • 2021
  • Ingår i: Alexandria Engineering Journal. - Netherlands : Elsevier. - 1110-0168 .- 2090-2670. ; 60:2, s. 2193-2208
  • Tidskriftsartikel (refereegranskat)abstract
    • In the present study, an improved adaptive neuro fuzzy inference system (ANFIS) and multilayer perceptron (MLP) models are hybridized with a sunflower optimization (SO) algorithm and are introduced for lake water level simulation. The Urmia Lake water level is predicted and assessed using the potential of the proposed advanced artificial intelligence (AI) models. The sunflower optimization algorithm is implemented to find the optimal tuning parameters. The results indicated that the ANFIS-SO model with the combination of three lags of rainfall and temperature as input attributes attained the best predictability performance. The minimal values of the root mean square error were RMSE = 1.89 m and 1.92 m for the training and testing modeling phases, respectively. The worst prediction capacity was attained for the long lead (i.e., six months rainfall lag times). The uncertainty analysis showed that the ANFIS-SO model had less uncertainty based on the percentage of more responses in the confidence band and lower bandwidth. Also, different scenarios of water harvesting were investigated with the consideration of environmental restrictions and fair water allocation to stakeholders. Further, studying Urmia Lake water harvesting scenarios displayed that the 30% water harvesting scenario of the lake water improves the lake’s water level.
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17.
  • Falih, Ali Hasan, et al. (författare)
  • Comparative study on salinity removal methods: an evaluation-based stable isotopes signatures in ground and sea water
  • 2023
  • Ingår i: Applied water science. - : Springer. - 2190-5487 .- 2190-5495. ; 13
  • Tidskriftsartikel (refereegranskat)abstract
    • This research aims to attain the optimal method of removing the high salinity concentrations without its effect on the balance or accuracy of stable isotopes measurement of deuterium and oxygen-18 (δ18O, δ2H). Four treatment methods (i.e., distillation, vacuum distillation, electro dialysis and ion exchange) were applied for nine samples, which were obtained from different water sources (sea, groundwater, river).l Worth to notice that the samples have Electrical Conductivity (EC) ranged (1000–60,000 µs/cm). Liquid–Water Isotope Analyzer used to measure the isotope concentration of δ18O, δ2H. The research findings of the four applied methods revealed their effectiveness with various percentages (normal distillation: 92.37%; vacuum distillation: 88.31%; electro dialysis: 94.85%; ion exchange: 99.62%). In addition, the investigation was conducted a clear correspondence measurement of (δ18O, δ2H) isotopes before and after treatment. The four methods results indicated that samples with EC ranged (1000–5000 µs/cm) have no effect on stable isotope readings. Whereas, samples with EC higher than 10,000, have substantial influence on the stable isotope readings. Finally, vacuum distillation method attained the best results among the treatment methods for EC ranged (10,000–60,000 µs/cm) without affecting the isotopic content of (δ18O, δ2H). There is a clear correspondence of the stable isotopic measurements before and after treatment, for all the selected samples.
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18.
  • Fang, Yin, et al. (författare)
  • An accelerated gradient-based optimization development for multi-reservoir hydropower systems optimization
  • 2021
  • Ingår i: Energy Reports. - : Elsevier BV. - 2352-4847. ; 7, s. 7854-7877
  • Tidskriftsartikel (refereegranskat)abstract
    • Hydropower is one of the significant renewable energy resources. It is regularly requested at peak time steps to meet the load requirements of power systems resources allocation. Therefore, modeling the optimal operation of hydropower systems to maximize the entire energy production of reservoir systems can be a vital task for energy investment. Deriving optimal unknown decision parameters of these reservoir systems is a nonlinear, nonconvex, and complex optimization problem. Herein, a novel optimization algorithm, called an accelerated version of gradient-based optimization (AGBO), is developed to solve a complex multi-reservoir hydropower system. This advised technique uses an efficient adaptive control parameters mechanism to stabilize the global and local search; utilizing an enhanced local escaping operator (ELEO) to extend the chances of getting away from local optima; expanding the exploitation search by applying the sequential quadratic programming (SQP) technique. At first, the developed AGBO algorithm is employed to solve the optimal operation of a complex 10-reservoir hydropower system. Secondly, the possibility of the AGBO algorithm within the global optimization problems is illustrated by numerical tests of 23 mathematical benchmark functions. Optimal results show that the proposed AGBO can approach to 0.9999% of the optimal global solution. As a result, the advised method is the most superior one compared to the other advanced optimization algorithms for maximizing the load demands in hydropower system. In conclusion, this offers a productive tool to solve the complex hydropower multi-reservoir optimization systems.
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19.
  • Fu, Minglei, et al. (författare)
  • Deep Learning Data-Intelligence Model Based on Adjusted Forecasting Window Scale : Application in Daily Streamflow Simulation
  • 2020
  • Ingår i: IEEE Access. - USA : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 8:1, s. 32632-32651
  • Tidskriftsartikel (refereegranskat)abstract
    • Streamow forecasting is essential for hydrological engineering. In accordance with theadvancement of computer aids in this eld, various machine learning (ML) models have been explored tosolve this highly non-stationary, stochastic, and nonlinear problem. In the current research, a newly exploredversion of an ML model called the long short-term memory (LSTM) was investigated for streamowprediction using historical data for forecasting for a particular period. For a case study located in a tropicalenvironment, the Kelantan river in the northeast region of the Malaysia Peninsula was selected. Themodelling was performed according to several perspectives: (i) The feasibility of applying the developedLSTM model to streamow prediction was veried, and the performance of the developed LSTM modelwas compared with the classic backpropagation neural network model; (ii) In the experimental process ofapplying the LSTM model to the prediction of streamow, the inuence of the training set size on theperformance of the developed LSTM model was tested; (iii) The effect of the time interval between thetraining set and the testing set on the performance of the developed LSTM model was tested; (iv) The effectof the time span of the prediction data on the performance of the developed LSTM model was tested. Theexperimental data showthat not only does the developedLSTM model have obvious advantages in processingsteady streamow data in the dry season but it also shows good ability to capture data features in the rapidlyuctuant streamow data in the rainy season.
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20.
  • Hadi, Sinan Jasim, et al. (författare)
  • Non-linear input variable selection approach integrated with non-tuned data intelligence model for streamflow pattern simulation
  • 2019
  • Ingår i: IEEE Access. - USA : IEEE. - 2169-3536. ; 7, s. 141533-141548
  • Tidskriftsartikel (refereegranskat)abstract
    • Streamflow modeling is considered as an essential component for water resources planning and management. There are numerous challenges related to streamflow prediction that are facing water resources engineers. These challenges due to the complex processes associated with several natural variables such as non-stationarity, non-linearity, and randomness. In this study, a new model is proposed to predict long-term streamflow. Several lags that cover several years are abstracted using the potential of Extreme Gradient Boosting (XGB) then after the selected inputs variables are imposed into the predictive model (i.e., Extreme Learning Machine (ELM)). The proposed model is compared with the stand-alone schema in which the optimum lags of the variables are supplied into the XGB and ELM models. Hydrological variables including rainfall, temperature and evapotranspiration are used to build the model and predict the streamflow at Goksu-Himmeti basin in Turkey. The results showed that XGB model performed an excellent result in which can be used for predicting the streamflow pattern. Also, it is clear from the attained results that the accuracy of the streamflow prediction using XGB technique could be improved when the high number of lags was used. However, the implementation of the XGB is tree-based technique in which several issues could be raised such as overfitting problem. The proposed schema XGBELM in which XGB approach is selected the correlated inputs and ranking them according to their importance; then after, the selected inputs are supplied into the ELM model for the prediction process. The XGBELM model outperformed the stand-alone schema of both XGB and ELM models and the high-lagged schema of the XGB. It is important to indicate that the XGBELM model found to improve the prediction ability with minimum variables number.
  •  
21.
  • Hadi, Sinan Jasim, et al. (författare)
  • The Capacity of the Hybridizing Wavelet Transformation Approach With Data-Driven Models for Modeling Monthly-Scale Streamflow
  • 2020
  • Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 8, s. 101993-102006
  • Tidskriftsartikel (refereegranskat)abstract
    • Hybrid models that combine wavelet transformation (WT) as a pre-processing tool with data-driven models (DDMs) as modeling approaches have been widely investigated for forecasting streamflow. The WT approach has been applied to original time series for decomposing processes prior to the application of DDM modeling. This procedure has been applied to eliminate redundant patterns or information that lead to a dramatic increase in the model performance. In this study, three experiments were implemented, including stand-alone data-driven modeling, hind cast decomposing using WT divided and entered into the extreme learning machine (ELM), and the extreme gradient boosting (XGB) model to forecast streamflow data. The WT method was applied in two forms: discrete and continuous (DWT and CWT). In this paper, a new hybrid model is proposed based on an integrative prediction model where XGB is used as an input selection tool for the importance attributes of the prediction matrix that are then supplied to the ELM model as a predictive model. The monthly streamflow, upstream flow, rainfall, temperature, and potential evapotranspiration of a basin named in 1805 and located in the south east of Turkey, are used for development of the model. The modeling results show that applying the WT method improved the performance in the hindcast experiment based on the CWT form with minimum root mean square error (RMSE = 4.910 m 3 /s). On the contrary, WT deteriorated the performance of the forecasting and the stand-alone models exhibited a better performance. WT increased the performance of the hindcast experiment due to the inclusion of future information caused by convolution of the time series. However, the forecast experiment experienced deterioration due to the border effect at the end of the time series. Hence, WT was found not to be a useful pre-processing technique in forecasting the streamflow.
  •  
22.
  • Hai, Tao, et al. (författare)
  • Global Solar Radiation Estimation and Climatic Variability Analysis Using Extreme Learning Machine Based Predictive Model
  • 2020
  • Ingår i: IEEE Access. - USA : IEEE. - 2169-3536. ; 8, s. 12026-12042
  • Tidskriftsartikel (refereegranskat)abstract
    • Sustainable utilization of the freely available solar radiation as renewable energy source requires accurate predictive models to quantitatively evaluate future energy potentials. In this research, an evaluation of the preciseness of extreme learning machine (ELM) model as a fast and efficient framework for estimating global incident solar radiation (G) is undertaken. Daily meteorological datasets suitable for G estimation belongs to the northern parts of the Cheliff Basin in Northwest Algeria, is used to construct the estimation model. Cross-correlation functions are applied between the inputs and the target variable (i.e., G) where several climatological information’s are used as the predictors for surface level G estimation. The most significant model inputs are determined in accordance with highest cross-correlations considering the covariance of the predictors with the G dataset. Subsequently, seven ELM models with unique neuronal architectures in terms of their input-hidden-output neurons are developed with appropriate input combinations. The prescribed ELM model’s estimation performance over the testing phase is evaluated against multiple linear regressions (MLR), autoregressive integrated moving average (ARIMA) models and several well-established literature studies. This is done in accordance with several statistical score metrics. In quantitative terms, the root mean square error (RMSE) and mean absolute error (MAE) are dramatically lower for the optimal ELM model with RMSE and MAE = 3.28 and 2.32 Wm −2 compared to 4.24 and 3.24 Wm −2 (MLR) and 8.33 and 5.37 Wm −2 (ARIMA).
  •  
23.
  • Halder, Bijay, et al. (författare)
  • Machine learning-based country-level annual air pollutants exploration using Sentinel-5P and Google Earth Engine
  • 2023
  • Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 13
  • Tidskriftsartikel (refereegranskat)abstract
    • Climatic condition is triggering human health emergencies and earth’s surface changes. Anthropogenic activities, such as built-up expansion, transportation development, industrial works, and some extreme phases, are the main reason for climate change and global warming. Air pollutants are increased gradually due to anthropogenic activities and triggering the earth’s health. Nitrogen Dioxide (NO2), Carbon Monoxide (CO), and Aerosol Optical Depth (AOD) are truthfully important for air quality measurement because those air pollutants are more harmful to the environment and human’s health. Earth observational Sentinel-5P is applied for monitoring the air pollutant and chemical conditions in the atmosphere from 2018 to 2021. The cloud computing-based Google Earth Engine (GEE) platform is applied for monitoring those air pollutants and chemical components in the atmosphere. The NO2 variation indicates high during the time because of the anthropogenic activities. Carbon Monoxide (CO) is also located high between two 1-month different maps. The 2020 and 2021 results indicate AQI change is high where 2018 and 2019 indicates low AQI throughout the year. The Kolkata have seven AQI monitoring station where high nitrogen dioxide recorded 102 (2018), 48 (2019), 26 (2020) and 98 (2021), where Delhi AQI stations recorded 99 (2018), 49 (2019), 37 (2020), and 107 (2021). Delhi, Kolkata, Mumbai, Pune, and Chennai recorded huge fluctuations of air pollutants during the study periods, where ~ 50–60% NO2 was recorded as high in the recent time. The AOD was noticed high in Uttar Pradesh in 2020. These results indicate that air pollutant investigation is much necessary for future planning and management otherwise; our planet earth is mostly affected by the anthropogenic and climatic conditions where maybe life does not exist.
  •  
24.
  • Hou, Muzhou, et al. (författare)
  • Global Solar Radiation Prediction Using Hybrid Online Sequential Extreme Learning Machine Model
  • 2018
  • Ingår i: Energies. - : MDPI. - 1996-1073. ; 11:12
  • Tidskriftsartikel (refereegranskat)abstract
    • Accurate global solar radiation prediction is highly essential for related research on renewable energy sources. The cost implication and measurement expertise of global solar radiationemphasize that intelligence prediction models need to be applied. On the basis of long-term measured daily solar radiation data, this study uses a novel regularized online sequential extreme learning machine, integrated with variable forgetting factor (FOS-ELM), to predict global solar radiation at Bur Dedougou, in the Burkina Faso region. Bayesian Information Criterion (BIC) is applied to build the seven input combinations based on speed (Wspeed), maximum and minimum temperature (Tmax and Tmin), maximum and minimum humidity (Hmax and Hmin), evaporation (Eo) and vapor pressure deficiency (VPD). For the difference input parameters magnitudes, seven models were developed and evaluated for the optimal input combination. Various statistical indicators were computed for the prediction accuracy examination. The experimental results of the applied FOS-ELM model demonstrated a reliable prediction accuracy against the classical extreme learning machine (ELM) model for daily global solar radiation simulation. In fact, compared to classical ELM, the FOS-ELM model reported an enhancement in the root mean square error (RMSE) and mean absolute error(MAE) by (68.8–79.8%). In summary, the results clearly confirm the effectiveness of the FOS-ELM model, owing to the fixed internal tuning parameters.
  •  
25.
  • Jing, Wang, et al. (författare)
  • Implementation of evolutionary computing models for reference evapotranspiration modeling : short review, assessment and possible future research directions
  • 2019
  • Ingår i: Engineering Applications of Computational Fluid Mechanics. - UK : Taylor & Francis. - 1994-2060 .- 1997-003X. ; 13:1, s. 811-823
  • Tidskriftsartikel (refereegranskat)abstract
    • Evapotranspiration is one of the most important components of the hydrological cycle as it accounts for more than two-thirds of the global precipitation losses. Indeed, the accurate prediction of reference evapotranspiration (ETo) is highly significant for many watershed activities, including agriculture, water management, crop production and several other applications. Therefore, reliable estimation of ETo is a major concern in hydrology. ETo can be estimated using different approaches, including field measurement, empirical formulation and mathematical equations. Most recently, advanced machine learning models have been developed for the estimation of ETo. Among several machine learning models, evolutionary computing (EC) has demonstrated a remarkable progression in the modeling of ETo. The current research is devoted to providing a new milestone in the implementation of the EC algorithm for the modeling of ETo. A comprehensive review is conducted to recognize the feasibility of EC models and their potential in simulating ETo in a wide range of environments. Evaluation and assessment of the models are also presented based on the review. Finally, several possible future research directions are proposed for the investigations of ETo using EC.
  •  
26.
  • Maghrebi, Mohsen, et al. (författare)
  • Iran's Agriculture in the Anthropocene
  • 2020
  • Ingår i: Earth's Future. - : John Wiley & Sons. - 2328-4277. ; 8:9
  • Tidskriftsartikel (refereegranskat)abstract
    • The anthropogenic impacts of development and frequent droughts have limited Iran's water availability. This has major implications for Iran's agricultural sector which is responsible for about 90% of water consumption at the national scale. This study investigates if declining water availability impacted agriculture in Iran. Using the Mann‐Kendall and Sen's slope estimator methods, we explored the changes in Iran's agricultural production and area during the 1981‐2013 period. Despite decreasing water availability during this period, irrigated agricultural production and area continuously increased. This unsustainable agricultural development, which would have been impossible without the over‐abstraction of surface and ground water resources, has major long‐term water, food, environmental, and human security implications for Iran.
  •  
27.
  • Malik, Anurag, et al. (författare)
  • Modeling monthly pan evaporation process over the Indian central Himalayas : application of multiple learning artificial intelligence model
  • 2020
  • Ingår i: Engineering Applications of Computational Fluid Mechanics. - UK : Taylor & Francis. - 1994-2060 .- 1997-003X. ; 14:1, s. 323-338
  • Tidskriftsartikel (refereegranskat)abstract
    • The potential of several predictive models including multiple model-artificial neural network (MM-ANN), multivariate adaptive regression spline (MARS), support vector machine (SVM), multi-gene genetic programming (MGGP), and ‘M5Tree’ were assessed to simulate the pan evaporation in monthly scale (EPm) at two stations (e.g. Ranichauri and Pantnagar) in India. Monthly climatological information were used for simulating the pan evaporation. The utmost effective input-variables for the MM-ANN, MGGP, MARS, SVM, and M5Tree were determined using the Gamma test (GT). The predictive models were compared to each other using several statistical criteria (e.g. mean absolute percentage error (MAPE), Willmott's Index of agreement (WI), root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), and Legate and McCabe’s Index (LM)) and visual inspection. The results showed that the MM-ANN-1 and MGGP-1 models (NSE, WI, LM, RMSE, MAPE are 0.954, 0.988, 0.801, 0.536 mm/month, 9.988% at Pantnagar station, and 0.911, 0.975, 0.724, and 0.364 mm/month, 12.297% at Ranichauri station, respectively) with input variables equal to six were more successful than the other techniques during testing period to simulate the monthly pan evaporation at both Ranichauri and Pantnagar stations. Thus, the results of proposed MM-ANN-1 and MGGP-1 models will help to the local stakeholders in terms of water resources management.
  •  
28.
  • Malik, Anurag, et al. (författare)
  • Pan Evaporation Estimation in Uttarakhand and Uttar Pradesh States, India : Validity of an Integrative Data Intelligence Model
  • 2020
  • Ingår i: Atmosphere. - Switzerland : MDPI. - 2073-4433. ; 11:6
  • Tidskriftsartikel (refereegranskat)abstract
    • Appropriate input selection for the estimation matrix is essential when modeling non-linear progression. In this study, the feasibility of the Gamma test (GT) was investigated to extract the optimal input combination as the primary modeling step for estimating monthly pan evaporation (EPm). A new artificial intelligent (AI) model called the co-active neuro-fuzzy inference system (CANFIS) was developed for monthly EPm estimation at Pantnagar station (located in Uttarakhand State) and Nagina station (located in Uttar Pradesh State), India. The proposed AI model was trained and tested using different percentages of data points in scenarios one to four. The estimates yielded by the CANFIS model were validated against several well-established predictive AI (multilayer perceptron neural network (MLPNN) and multiple linear regression (MLR)) and empirical (Penman model (PM)) models. Multiple statistical metrics (normalized root mean square error (NRMSE), Nash–Sutcliffe efficiency (NSE), Pearson correlation coefficient (PCC), Willmott index (WI), and relative error (RE)) and graphical interpretation (time variation plot, scatter plot, relative error plot, and Taylor diagram) were performed for the modeling evaluation. The results of appraisal showed that the CANFIS-1 model with six input variables provided better NRMSE (0.1364, 0.0904, 0.0947, and 0.0898), NSE (0.9439, 0.9736, 0.9703, and 0.9799), PCC (0.9790, 0.9872, 0.9877, and 0.9922), and WI (0.9860, 0.9934, 0.9927, and 0.9949) values for Pantnagar station, and NRMSE (0.1543, 0.1719, 0.2067, and 0.1356), NSE (0.9150, 0.8962, 0.8382, and 0.9453), PCC (0.9643, 0.9649, 0.9473, and 0.9762), and WI (0.9794, 0.9761, 0.9632, and 0.9853) values for Nagina stations in all applied modeling scenarios for estimating the monthly EPm. This study also confirmed the supremacy of the proposed integrated GT-CANFIS model under four different scenarios in estimating monthly EPm. The results of the current application demonstrated a reliable modeling methodology for water resource management and sustainability.
  •  
29.
  • Milad, Abdalrhman, et al. (författare)
  • An Educational Web-Based Expert System for Novice Highway Technology in Flexible Pavement Maintenance
  • 2021
  • Ingår i: Complexity. - UK : Hindawi Publishing Corporation. - 1076-2787 .- 1099-0526. ; 2021
  • Tidskriftsartikel (refereegranskat)abstract
    • Nowadays, higher education worldwide is affected by the COVID-19 pandemic. It has affected students’ attendance in the universities and causes universities to close down in more than 190 countries. On the other hand, novice engineers studied only a few lectures related to highway engineering. Their lectures have included very little knowledge about asphalt pavement construction as highway engineering consists of many areas that are not studied in detail during their studying years subject to their traditional education. Due to all mentioned, a new drive to promote online learning paves the way to evaluate our future approach to curriculum development and delivery of educational materials for engineering courses. However, experts can offer solutions to these problems using their past experience. Hence, a system that allows experts to share their experience with other engineers after completing a project is needed. Nevertheless, the web-based expert system for maintaining flexible pavement problems in tropical regions (ESTAMPSYS) designed in this study is a novel concept. Prior to developing this system, the need for such a system was determined through literature review and validated through a questionnaire survey. Experts were interviewed, and a questionnaire survey was conducted to construct the knowledge base of the system. Knowledge was presented as rules and coded in software through PHP programming. Web pages that support the user interface were designed using a framework that consists of CSS, HTML, and J-Query. Furthermore, the system was tested by an array of users engaged in highway engineering, namely, experts, teaching experts, novice engineers, and students. The mean values of the overall system evaluation performed by 20 users using a five-point Likert scale were 4, 4.5, 3.75, 4.25, 5, 4, and 3.5. Expert and user satisfaction prove the effectiveness of the proposed system.
  •  
30.
  • Milad, Abdalrhman, et al. (författare)
  • Emerging Technologies of Deep Learning Models Development for Pavement Temperature Prediction
  • 2021
  • Ingår i: IEEE Access. - USA : IEEE. - 2169-3536. ; 9, s. 23840-23849
  • Tidskriftsartikel (refereegranskat)abstract
    • Air temperature is one of the critical factors influencing the bearing ability and performance of temperature-sensitive asphalt materials. This research investigates the relationship between air temperature at different depths and time to predict asphalt pavement temperature and evaluate asphalt performance. This paper discusses four deep learning-based regression models for calculating asphalt pavement temperature based on air temperature, depth from the asphalt surface, and time. Measurement of pavement temperature was made in the Gaza Strip. Monitoring stations were set up to measure asphalt pavement temperature and air temperature at different depths and times. The data were collected by hand measurement for the period from March 2012 to February 2013. The data is trained and validated using the Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), and Gated Recurrent Unit (GRU). Bi-LSTM has an R 2 of 0.9555 for the generated dataset and outperforms other algorithms because of its superiority in feature extraction and multidimensional data processing. Through deep learning techniques, Bi-LSTM has demonstrated outstanding robustness and promising potential in predicting asphalt pavement temperature.
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31.
  • Mohammad, Reem Sabah, et al. (författare)
  • Frictional pressure drop and cost savings for graphene nanoplatelets nanofluids in turbulent flow environments
  • 2021
  • Ingår i: Nanomaterials. - : MDPI AG. - 2079-4991. ; 11:11
  • Tidskriftsartikel (refereegranskat)abstract
    • Covalent-functionalized graphene nanoplatelets (CF-GNPs) inside a circular heated-pipe and the subsequent pressure decrease loss within a fully developed turbulent flow were discussed in this research. Four samples of nanofluids were prepared and investigated in the ranges of 0.025 wt.%, 0.05 wt.%, 0.075 wt.%, and 0.1 wt.%. Different tools such as field emission scanning electron microscopy (FE-SEM), ultraviolet-visible-spectrophotometer (UV-visible), energy-dispersive X-ray spectroscopy (EDX), zeta potential, and nanoparticle sizing were used for the data preparation. The thermophysical properties of the working fluids were experimentally determined using the testing conditions established via computational fluid dynamic (CFD) simulations that had been designed to solve governing equations involving distilled water (DW) and nanofluidic flows. The average error between the numerical solution and the Blasius formula was ~4.85%. Relative to the DW, the pressure dropped by 27.80% for 0.025 wt.%, 35.69% for 0.05 wt.%, 41.61% for 0.075 wt.%, and 47.04% for 0.1 wt.%. Meanwhile, the pumping power increased by 3.8% for 0.025 wt.%, 5.3% for 0.05 wt.%, 6.6% for 0.075%, and 7.8% for 0.1 wt.%. The research findings on the cost analysis demonstrated that the daily electric costs were USD 214, 350, 416, 482, and 558 for DW of 0.025 wt.%, 0.05 wt.%, 0.075 wt.%, and 0.1 wt.%, respectively.
  •  
32.
  • Naganna, Sujay Raghavendra, et al. (författare)
  • Dew Point Temperature Estimation : Application of Artificial Intelligence Model Integrated with Nature-Inspired Optimization Algorithms
  • 2019
  • Ingår i: Water. - Switzerland : MDPI. - 2073-4441. ; 11:4
  • Tidskriftsartikel (refereegranskat)abstract
    • Dew point temperature (DPT) is known to fluctuate in space and time regardless of the climatic zone considered. The accurate estimation of the DPT is highly significant for various applications of hydro and agro–climatological researches. The current research investigated the hybridization of a multilayer perceptron (MLP) neural network with nature-inspired optimization algorithms (i.e., gravitational search (GSA) and firefly (FFA)) to model the DPT of two climatically contrasted (humid and semi-arid) regions in India. Daily time scale measured weather information, such as wet bulb temperature (WBT), vapor pressure (VP), relative humidity (RH), and dew point temperature, was used to build the proposed predictive models. The efficiencies of the proposed hybrid MLP networks (MLP–FFA and MLP–GSA) were authenticated against standard MLP tuned by a Levenberg–Marquardt back-propagation algorithm, extreme learning machine (ELM), and support vector machine (SVM) models. Statistical evaluation metrics such as Nash Sutcliffe efficiency (NSE), root mean square error (RMSE), and mean absolute error (MAE) were used to validate the model efficiency. The proposed hybrid MLP models exhibited excellent estimation accuracy. The hybridization of MLP with nature-inspired optimization algorithms boosted the estimation accuracy that is clearly owing to the tuning robustness. In general, the applied methodology showed very convincing results for both inspected climate zones.
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33.
  • Penghui, Liu, et al. (författare)
  • Metaheuristic Optimization Algorithms Hybridized With Artificial Intelligence Model for Soil Temperature Prediction : Novel Model
  • 2020
  • Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 8, s. 51884-51904
  • Tidskriftsartikel (refereegranskat)abstract
    • An enhanced hybrid articial intelligence model was developed for soil temperature (ST) prediction. Among several soil characteristics, soil temperature is one of the essential elements impacting the biological, physical and chemical processes of the terrestrial ecosystem. Reliable ST prediction is signicant for multiple geo-science and agricultural applications. The proposed model is a hybridization of adaptive neuro-fuzzy inference system with optimization methods using mutation Salp Swarm Algorithm and Grasshopper Optimization Algorithm (ANFIS-mSG). Daily weather and soil temperature data for nine years (1 of January 2010 - 31 of December 2018) from ve meteorological stations (i.e., Baker, Beach, Cando, Crary and Fingal) in North Dakota, USA, were used for modeling. For validation, the proposed ANFIS-mSG model was compared with seven models, including classical ANFIS, hybridized ANFIS model with grasshopper optimization algorithm (ANFIS-GOA), salp swarm algorithm (ANFIS-SSA), grey wolf optimizer (ANFIS-GWO), particle swarm optimization (ANFIS-PSO), genetic algorithm (ANFIS-GA),and Dragon y Algorithm (ANFIS-DA). The ST prediction was conducted based on maximum, mean and minimum air temperature (AT). The modeling results evidenced the capability of optimization algorithms for building ANFIS models for simulating soil temperature. Based on the statistical evaluation; for instance, the root mean square error (RMSE) was reduced by 73%, 74.4%, 71.2%, 76.7% and 80.7% for Baker, Beach, Cando, Crary and Fingal meteorological stations, respectively, throughout the testing phase when ANFIS-mSG was used over the standalone ANFIS models. In conclusion, the ANFIS-mSG model was demonstrated as an effective and simple hybrid articial intelligence model for predicting soil temperature based on univariate air temperature scenario.
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34.
  • Qutbudin, Ishanch, et al. (författare)
  • Seasonal Drought Pattern Changes Due to Climate Variability : Case Study in Afghanistan
  • 2019
  • Ingår i: Water. - : MDPI. - 2073-4441. ; 11:5, s. 1-20
  • Tidskriftsartikel (refereegranskat)abstract
    • We assessed the changes in meteorological drought severity and drought return periods during cropping seasons in Afghanistan for the period of 1901 to 2010. The droughts in the country were analyzed using the standardized precipitation evapotranspiration index (SPEI). Global Precipitation Climatology Center rainfall and Climate Research Unit temperature data both at 0.5 resolutions were used for this purpose. Seasonal drought return periods were estimated using the values of the SPEI fitted with the best distribution function. Trends in climatic variables and SPEI were assessed using modified Mann–Kendal trend test, which has the ability to remove the influence of long-term persistence on trend significance. The study revealed increases in drought severity and frequency in Afghanistan over the study period. Temperature, which increased up to 0.14 C/decade, was the major factor influencing the decreasing trend in the SPEI values in the northwest and southwest of the country during rice- and corn-growing seasons, whereas increasing temperature and decreasing rainfall were the cause of a decrease in SPEI during wheat-growing season. We concluded that temperature plays a more significant role in decreasing the SPEI values and, therefore, more severe droughts in the future are expected due to global warming.
  •  
35.
  • Salih, Sinan Q., et al. (författare)
  • Viability of the advanced adaptive neuro-fuzzy inference system model on reservoir evaporation process simulation : case study of Nasser Lake in Egypt
  • 2019
  • Ingår i: Engineering Applications of Computational Fluid Mechanics. - UK : Taylor & Francis. - 1994-2060 .- 1997-003X. ; 13:1, s. 878-891
  • Tidskriftsartikel (refereegranskat)abstract
    • Reliable prediction of evaporative losses from reservoirs is an essential component of reservoir management and operation. Conventional models generally used for evaporation prediction have a number of drawbacks as they are based on several assumptions. A novel approach called the co-active neuro-fuzzy inference system (CANFIS) is proposed in this study for the modeling of evaporation from meteorological variables. CANFIS provides a center-weighted set rather than global weight sets for predictor–predictand relationship mapping and thus it can provide a higher prediction accuracy. In the present study, adjustments are made in the back-propagation algorithm of CANFIS for automatic updating of membership rules and further enhancement of its prediction accuracy. The predictive ability of the CANFIS model is validated with three well-established artificial intelligence (AI) models. Different statistical metrics are computed to investigate the prediction efficacy. The results reveal higher accuracy of the CANFIS model in predicting evaporation compared to the other AI models. CANFIS is found to be capable of modeling evaporation from mean temperature and relative humidity only, with a Nash–Sutcliffe efficiency of 0.93, which is much higher than that of the other models. Furthermore, CANFIS improves the prediction accuracy by 9.2–55.4% compared to the other AI models.
  •  
36.
  • Salman, Saleem A., et al. (författare)
  • Changes in Climatic Water Availability and Crop Water Demand for Iraq Region
  • 2020
  • Ingår i: Sustainability. - Switzerland : MDPI. - 2071-1050. ; 12:8
  • Tidskriftsartikel (refereegranskat)abstract
    • Decreases in climatic water availability (CWA) and increases in crop water demand (CWD) in the background of climate change are a major concern in arid regions because of less water availability and higher irrigation requirements for crop production. Assessment of the spatiotemporal changes in CWA and CWD is important for the adaptation of irrigated agriculture to climate change for such regions. The recent changes in CWA and CWD during growing seasons of major crops have been assessed for Iraq where rapid changes in climate have been noticed in recent decades. Gridded precipitation of the global precipitation climatology center (GPCC) and gridded temperature of the climate research unit (CRU) having a spatial resolution of 0.5°, were used for the estimation of CWA and CWD using simple water balance equations. The Mann–Kendall (MK) test and one of its modified versions which can consider long-term persistence in time series, were used to estimate trends in CWA for the period 1961–2013. In addition, the changes in CWD between early (1961–1990) and late (1984–2013) periods were evaluated using the Wilcoxon rank test. The results revealed a deficit in water in all the seasons in most of the country while a surplus in the northern highlands in all the seasons except summer was observed. A significant reduction in the annual amount of CWA at a rate of −1 to −13 mm/year was observed at 0.5 level of significance in most of Iraq except in the north. Decreasing trends in CWA in spring (−0.4 to −1.8 mm/year), summer (−5.0 to −11 mm/year) and autumn (0.3 to −0.6 mm/year), and almost no change in winter was observed. The CWA during the growing season of summer crop (millet and sorghum) was found to decrease significantly in most of Iraq except in the north. The comparison of CWD revealed an increase in agricultural water needs in the late period (1984–2013) compared to the early period (1961–1990) by 1.0–8.0, 1.0–14, 15–30, 14–27 and 0.0–10 mm for wheat, barley, millet, sorghum and potato, respectively. The highest increase in CWD was found in April, October, June, June and April for wheat, barley, millet, sorghum and potato, respectively.
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37.
  • Sharafati, Ahmad, et al. (författare)
  • Assessing the Uncertainty Associated with Flood Features due to Variability of Rainfall and Hydrological Parameters
  • 2020
  • Ingår i: Advances in Civil Engineering / Hindawi. - London : Hindawi Publishing Corporation. - 1687-8086 .- 1687-8094. ; 2020
  • Tidskriftsartikel (refereegranskat)abstract
    • An assessment of uncertainty in flood hydrograph features, e.g., peak discharge and flood volume due to variability in the rainfall-runoff model (HEC-HMS) parameters and rainfall characteristics, e.g., depth and duration, is conducted. Flood hydrographs are generated using a rain pattern generator (RPG) and HEC-HMS models through Monte Carlo simulation considering uncertainty in stochastic variables. The uncertainties in HEC-HMS parameters (e.g., loss, base flow, and unit hydrograph) are estimated using their probability distribution functions. The flood events are obtained by simulating runoff for rainfall events using the generated model parameters. The uncertainties due to rainfall and model parameters on generated flood hydrographs are evaluated using the relative coefficient of variation (RCV). The results reveal a higher RCV index for flood volume (RCV = 153) than peak discharge (RCV = 116) for a 12-hr rainfall duration. The average relative RCV (ARRCV) index computed for hydrological component (e.g., base flow, loss, or unit hydrograph) indicates the highest impact of rainfall depth on flood volume and peak. The results indicate that rainfall depth is the main source of uncertainty of flood peak and volume.
  •  
38.
  • Sharafati, Ahmad, et al. (författare)
  • Development of Advanced Computer Aid Model for Shear Strength of Concrete Slender Beam Prediction
  • 2020
  • Ingår i: Applied Sciences. - Switzerland : MDPI. - 2076-3417. ; 10:11
  • Tidskriftsartikel (refereegranskat)abstract
    • High-strength concrete (HSC) is highly applicable to the construction of heavy structures. However, shear strength (Ss) determination of HSC is a crucial concern for structure designers and decision makers. The current research proposes the novel models based on the combination of adaptive neuro-fuzzy inference system (ANFIS) with several meta-heuristic optimization algorithms, including ant colony optimizer (ACO), differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO), to predict the Ss of HSC slender beam. The proposed models were constructed using several input combinations incorporating several related dimensional parameters such as effective depth of beam (d), shear span (a), maximum size of aggregate (ag), compressive strength of concrete (fc), and percentage of tension reinforcement (ρ). To assess the impact of the non-homogeneity of the dataset on the prediction result accuracy, two possible modeling scenarios, (i) non-processed (initial) dataset (NP) and (ii) pre-processed dataset (PP), are inspected by several performance indices. The modeling results demonstrated that ANFIS-PSO hybrid model attained the best prediction accuracy over the other models and for the pre-processed input parameters. Several uncertainty analyses were examined (i.e., model, variables, and data), and results indicated predicting the HSC shear strength was more sensitive to the model structure uncertainty than the input parameters.
  •  
39.
  • Sharafati, Ahmad, et al. (författare)
  • Performance evaluation of sediment ejector efficiency using hybrid neuro-fuzzy models
  • 2021
  • Ingår i: Engineering Applications of Computational Fluid Mechanics. - : Taylor & Francis. - 1994-2060 .- 1997-003X. ; 15:1, s. 627-643
  • Tidskriftsartikel (refereegranskat)abstract
    • Sediment transport in the ejector is highly stochastic and non-linear in nature, and its accurate estimation is a complex and challenging mission. This study attempts to investigate the sediment removal estimation of sediment ejector using newly developed hybrid data-intelligence models. The proposed models are based on the hybridization of adaptive neuro-fuzzy inference systems (ANFIS) with different metaheuristic algorithms, namely, particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), and ant colony optimization (ACO). The proposed models are constructed with various related input variables such as sediment concentration, flow depth, velocity, sediment size, Froude number, extraction ratio, number of tunnels and sub-tunnels, and flow depth at upstream of the sediment ejector. The estimation capacity of the developed hybrid models is assessed using several statistical evaluation indices. The modeling results obtained for the studied ejector sediment removal estimation demonstrated an optimistic finding. Among the developed hybrid models, ANFIS-PSO model exhibited the best predictability potential with maximum correlation coefficient values CC Train = 0.915 and CCTest = 0.916. © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
  •  
40.
  • Sharafati, Ahmad, et al. (författare)
  • Scouring Depth Assessment Downstream of Weirs Using Hybrid Intelligence Models
  • 2020
  • Ingår i: Applied Sciences. - Switzerland : MDPI. - 2076-3417. ; 10:11
  • Tidskriftsartikel (refereegranskat)abstract
    • Considering the scouring depth downstream of weirs is a challenging issue due to its effect on weir stability. The adaptive neuro-fuzzy inference systems (ANFIS) model integrated with optimization methods namely cultural algorithm, biogeography based optimization (BBO), invasive weed optimization (IWO) and teaching learning based optimization (TLBO) are proposed to predict the maximum depth of scouring based on the different input combinations. Several performance indices and graphical evaluators are employed to estimate the prediction accuracy in the training and testing phase. Results show that the ANFIS-IWO offers the highest prediction performance (RMSE = 0.148) compared to other models in the testing phase, while the ANFIS-BBO (RMSE = 0.411)ANFIS-TLBO-M3 RMSEtesting=0.411, CCtesting~0.00) provides the lowest accuracy. The findings obtained from the uncertainty analysis of prediction modeling indicate that the input variables variability R-factor=1.72has a higher impact on the predicted results than the structure of models. In general, the ANFIS-IWO can be used as a reliable and cost-effective method for predicting the scouring depth downstream of weirs.
  •  
41.
  • Sulaiman, Sadeq Oleiwi, et al. (författare)
  • Simulation Model for Optimal Operation of Dokan Dam Reservoir Northern of Iraq
  • 2021
  • Ingår i: International Journal of Design & Nature and Ecodynamics. - : International Information and Engineering Technology Association (IIETA). - 1755-7437 .- 1755-7445. ; 16:3, s. 301-306
  • Tidskriftsartikel (refereegranskat)abstract
    • Due to the limitation of water renewable resources on one hand and increasing growth in consuming water in different parts such as agriculture, industry, urban, and the environment in other hand, face management of these valuable resources to many challenges. Present study attempts to clarify recent condition of the problem and introduce effective management tools in water supply sector. In order to achieve this purpose, simulating model HEC-Res Sim was used for Dokan Dam to study the operational behavior of the reservoir and to investigate the model capability in representing and simulating the real system. The study based on monthly discharge data for the period from 1986 to 2016 measured at the inlet of Dokan Dam reservoir. The results of the current study were compared and evaluated against those counterparts observed data using two statistical metrics, correlation coefficient and Nash- Sutcliff coefficient efficiency. Moreover, an empirical formula was found linking the amount of inflow to the reservoir with the amount of outflow. The results showed that the HEC-ResSim 3.0 performed well in simulating the monthly discharges. Therefore, HEC-ResSim 3.0 could be used for better water system analysis in this study area.
  •  
42.
  • Surendran, U., et al. (författare)
  • FAO CROPWAT Model-Based Irrigation Requirements for Coconut to Improve Crop and Water Productivity in Kerala, India
  • 2019
  • Ingår i: Sustainability. - Switzerland : MDPI. - 2071-1050. ; 11:18
  • Tidskriftsartikel (refereegranskat)abstract
    • The irrigation requirements for coconut in Kerala are general in nature. This study determined the irrigation requirements for coconut, using CROPWAT based on agro-ecological zones (AEZs) for proposing the recommendations. The irrigation recommendations are generated based on the climatic, soil, and crop characteristics. The results showed that the irrigation requirements varied with the locations. Overall, for the state of Kerala, the irrigation requirements varied from 350 to 900 L of water per coconut palm, with the irrigation intervals ranging from three to nine days based on the AEZs. Moreover, this study also confirmed the variation of the water requirements observed within the districts. The quantity of water required per palm varied between 115 to 200 liters per day (LPD) per palm, which is lower than the existing recommendations of 175 to 300 LPD per palm. The proposed irrigation requirements appraised with the presently followed recommendations of the Kerala state, and its advantages discussed for improving the crop and water productivity. In nutshell, if the current recommendation is adopted, 30% of the water used for irrigation can be saved, as well as leading to an improvement in crop production.
  •  
43.
  • Tao, Hai, et al. (författare)
  • A Newly Developed Integrative Bio-Inspired Artificial Intelligence Model for Wind Speed Prediction
  • 2020
  • Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 8, s. 83347-83358
  • Tidskriftsartikel (refereegranskat)abstract
    • Accurate wind speed (WS) modelling is crucial for optimal utilization of wind energy. Numerical Weather Prediction (NWP) techniques, generally used for WS modelling are not only less cost-effective but also poor in predicting in shorter time horizon. Novel WS prediction models based on the multivariate empirical mode decomposition (MEMD), random forest (RF) and Kernel Ridge Regression (KRR) were constructed in this paper better accuracy in WS prediction. Particle swarm optimization algorithm (PSO) was employed to optimize the parameters of the hybridized MEMD model with RF (MEMD-PSO-RF) and KRR (MEMD-PSO-KRR) models. Obtained results were compared to those of the standalone RF and KRR models. The proposed methodology is applied for monthly WS prediction at meteorological stations of Iraq, Baghdad (Station1) and Mosul (Station2) for the period 1977-2013. Results showed higher accuracy of MEMD-PSO-RF model in predicting WS at both stations with a correlation coefficient (r) of 0.972 and r = 0.971 during testing phase at Station1 and Station2, respectively. The MEMD-PSO-KRR was found as the second most accurate model followed by Standalone RF and KRR, but all showed a competitive performance to the MEMD-PSO-RF model. The outcomes of this work indicated that the MEMD-PSO-RF model has a remarkable performance in predicting WS and can be considered for practical applications.
  •  
44.
  • Tao, Hai, et al. (författare)
  • Energy and cost management of different mixing ratios and morphologies on mono and hybrid nanofluids in collector technologies
  • 2023
  • Ingår i: Engineering Applications of Computational Fluid Mechanics. - : Taylor & Francis. - 1994-2060 .- 1997-003X. ; 17:1
  • Tidskriftsartikel (refereegranskat)abstract
    • The flat-plate solar collector (FPSC) three-dimensional (3D) model was used to numerically evaluate the energy and economic estimates. A laminar flow with 500 ≤ Re ≤ 1900, an inlet temperature of 293 K, and a solar flux of 1000 W/m2 were assumed the operating conditions. Two mono nanofluids, CuO-DW and Cu-DW, were tested with different shapes (Spherical, Cylindrical, Platelets, and Blades) and different volume fractions. Additionally, hybrid nanocomposites from CuO@Cu/DW with different shapes (Spherical, Cylindrical, Platelets and Blades), different mixing ratios (60% + 40%, 50% + 50% and 40% + 60%) and different volume fractions (1 volume%, 2 volume%, 3 volume% and 4 volume%) were compared with mono nanofluids. At 1 volume% and Re = 1900, CuO-Platelets demonstrated the highest pressure drop (33.312 Pa). CuO-Platelets achieved the higher thermal enhancement with (8.761%) at 1 vol.% and Re = 1900. CuO-Platelets reduced the size of the solar collector by 25.60%. Meanwhile, CuO@Cu-Spherical (40:60) needed a larger collector size with 16.69% at 4 vol.% and Re = 1900. CuO-Platelets with 967.61, CuO – Cylindrical with 976.76, Cu Platelets with 983.84, and Cu-Cylindrical with 992.92 presented the lowest total cost. Meanwhile, the total cost of CuO – Cu – Platelets with 60:40, 50:50, and 40:60 was 994.82, 996.18, and 997.70, respectively.
  •  
45.
  • Tao, Hai, et al. (författare)
  • Global solar radiation prediction over North Dakota using air temperature : Development of novel hybrid intelligence model
  • 2021
  • Ingår i: Energy Reports. - Netherland : Elsevier. - 2352-4847. ; 7, s. 136-157
  • Tidskriftsartikel (refereegranskat)abstract
    • Accurate solar radiation (SR) prediction is one of the essential prerequisites of harvesting solar energy. The current study proposed a novel intelligence model through hybridization of Adaptive Neuro-Fuzzy Inference System (ANFIS) with two metaheuristic optimization algorithms, Salp Swarm Algorithm (SSA) and Grasshopper Optimization Algorithm (GOA) (ANFIS-muSG) for global SR prediction at different locations of North Dakota, USA. The performance of the proposed ANFIS-muSG model was compared with classical ANFIS, ANFIS-GOA, ANFIS-SSA, ANFIS-Grey Wolf Optimizer (ANFIS-GWO), ANFIS-Particle Swarm Optimization (ANFIS-PSO), ANFIS-Genetic Algorithm (ANFIS-GA) and ANFISDragonfly Algorithm (ANFIS-DA). Consistent maximum, mean and minimum air temperature data for nine years (2010–2018) were used to build the models. ANFIS-muSG showed 25.7%–54.8% higher performance accuracy in terms of root mean square error compared to other models at different locations of the study areas. The model developed in this study can be employed for SR prediction from temperature only. The results indicate the potential of hybridization of ANFIS with the metaheuristic optimization algorithms for improvement of prediction ccuracy.
  •  
46.
  • Tao, Hai, et al. (författare)
  • Groundwater level prediction using machine learning models: A comprehensive review
  • 2022
  • Ingår i: Neurocomputing. - : Elsevier. - 0925-2312 .- 1872-8286. ; 489, s. 271-308
  • Forskningsöversikt (refereegranskat)abstract
    • Developing accurate soft computing methods for groundwater level (GWL) forecasting is essential for enhancing the planning and management of water resources. Over the past two decades, significant progress has been made in GWL prediction using machine learning (ML) models. Several review articles have been published, reporting the advances in this field up to 2018. However, the existing review articles do not cover several aspects of GWL simulations using ML, which are significant for scientists and practitioners working in hydrology and water resource management. The current review article aims to provide a clear understanding of the state-of-the-art ML models implemented for GWL modeling and the milestones achieved in this domain. The review includes all of the types of ML models employed for GWL modeling from 2008 to 2020 (138 articles) and summarizes the details of the reviewed papers, including the types of models, data span, time scale, input and output parameters, performance criteria used, and the best models identified. Furthermore, recommendations for possible future research directions to improve the accuracy of GWL prediction models and enhance the related knowledge are outlined.
  •  
47.
  • Tao, Hai, et al. (författare)
  • Influence of water based binary composite nanofluids on thermal performance of solar thermal technologies: sustainability assessments
  • 2023
  • Ingår i: Engineering Applications of Computational Fluid Mechanics. - : Taylor & Francis. - 1994-2060 .- 1997-003X. ; 17:1
  • Tidskriftsartikel (refereegranskat)abstract
    • Recent technological advances have made it possible to produce particles with nanometer dimensions that are uniformly and steadily suspended in traditional solar liquids and have enhanced the impact of thermo-physical parameters. In this research, a three-dimensional flat plate solar collector was built using a thin flat plate and a single working fluid pipe. The physical model was solved computationally under conditions of conjugated laminar forced convection in the range 500 ≤ Re ≤ 1900 and a heat flux of 1000 W/m2. Distilled water (DW) and different types of hybrid nanofluids (namely, 0.1%-Al2O3@Cu/DW, 0.1%-MWCNTs@Fe3O4/DW, 0.3%-MWCNTs@Fe3O4/DW, 0.5%-Ag@MgO/DW, 1%-Ag@MgO/DW, 1%-S1 and 1%-S2, where MWCNTs are multi-wall carbon nanotubes, S1 means 2CuO–1Cu and S2 means 1CuO–2Cu nanocomposites) were evaluated via a set of parameters. The numerical results revealed that, by increasing the working fluid velocity (the Reynolds number), the average heat transfer coefficient, pressure loss, heat gain and solar collector efficiency were increased. Meanwhile, outlet fluid temperature and flat plate surface temperature were decreased. At Re = 1900, 1%-S2 and 1%-S1 presented higher thermal performance enhancement by 44.28% and 36.72% relative to DW. Moreover, low thermal performance enhancement of 7.59% and 7.44% were reported by 0.1%-Al2O3@Cu/DW and 0.3%-MWCNTs@Fe3O4/DW, respectively.
  •  
48.
  • Tao, Hai, et al. (författare)
  • Machine learning algorithms for high-resolution prediction of spatiotemporal distribution of air pollution from meteorological and soil parameters
  • 2023
  • Ingår i: Environment International. - : Elsevier. - 0160-4120 .- 1873-6750. ; 175
  • Tidskriftsartikel (refereegranskat)abstract
    • This study uses machine learning (ML) models for a high-resolution prediction (0.1°×0.1°) of air fine particular matter (PM2.5) concentration, the most harmful to human health, from meteorological and soil data. Iraq was considered the study area to implement the method. Different lags and the changing patterns of four European Reanalysis (ERA5) meteorological variables, rainfall, mean temperature, wind speed and relative humidity, and one soil parameter, the soil moisture, were used to select the suitable set of predictors using a non-greedy algorithm known as simulated annealing (SA). The selected predictors were used to simulate the temporal and spatial variability of air PM2.5 concentration over Iraq during the early summer (May-July), the most polluted months, using three advanced ML models, extremely randomized trees (ERT), stochastic gradient descent backpropagation (SGD-BP) and long short-term memory (LSTM) integrated with Bayesian optimizer. The spatial distribution of the annual average PM2.5 revealed the population of the whole of Iraq is exposed to a pollution level above the standard limit. The changes in temperature and soil moisture and the mean wind speed and humidity of the month before the early summer can predict the temporal and spatial variability of PM2.5 over Iraq during May-July. Results revealed the higher performance of LSTM with normalized root-mean-square error and Kling-Gupta efficiency of 13.4% and 0.89, compared to 16.02% and 0.81 for SDG-BP and 17.9% and 0.74 for ERT. The LSTM could also reconstruct the observed spatial distribution of PM2.5 with MapCurve and Cramer's V values of 0.95 and 0.91, compared to 0.9 and 0.86 for SGD-BP and 0.83 and 0.76 for ERT. The study provided a methodology for forecasting spatial variability of PM2.5 concentration at high resolution during the peak pollution months from freely available data, which can be replicated in other regions for generating high-resolution PM2.5 forecasting maps.
  •  
49.
  • Tao, Hai, et al. (författare)
  • Thermohydraulic analysis of covalent and noncovalent functionalized graphene nanoplatelets in circular tube fitted with turbulators
  • 2022
  • Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 12
  • Tidskriftsartikel (refereegranskat)abstract
    • Covalent and non-covalent nanofluids were tested inside a circular tube fitted with twisted tape inserts with 45° and 90° helix angles. Reynolds number was 7000 ≤ Re ≤ 17,000, and thermophysical properties were assessed at 308 K. The physical model was solved numerically via a two-equation eddy-viscosity model (SST k-omega turbulence). GNPs-SDBS@DW and GNPs-COOH@DW nanofluids with concentrations (0.025 wt.%, 0.05 wt.% and 0.1 wt.%) were considered in this study. The twisted pipes' walls were heated under a constant temperature of 330 K. The current study considered six parameters: outlet temperature, heat transfer coefficient, average Nusselt number, friction factor, pressure loss, and performance evaluation criterion. In both cases (45° and 90° helix angles), GNPs-SDBS@DW nanofluids presented higher thermohydraulic performance than GNPs-COOH@DW and increased by increasing the mass fractions such as 1.17 for 0.025 wt.%, 1.19 for 0.05 wt.% and 1.26 for 0.1 wt.%. Meanwhile, in both cases (45° and 90° helix angles), the value of thermohydraulic performance using GNPs-COOH@DW was 1.02 for 0.025 wt.%, 1.05 for 0.05 wt.% and 1.02 for 0.1 wt.%.
  •  
50.
  • Yaseen, Zaher Mundher, et al. (författare)
  • Forecasting standardized precipitation index using data intelligence models : regional investigation of Bangladesh
  • 2021
  • Ingår i: Scientific Reports. - UK : Springer Nature. - 2045-2322. ; 11
  • Tidskriftsartikel (refereegranskat)abstract
    • A noticeable increase in drought frequency and severity has been observed across the globe due to climate change, which attracted scientists in development of drought prediction models for mitigation of impacts. Droughts are usually monitored using drought indices (DIs), most of which are probabilistic and therefore, highly stochastic and non-linear. The current research investigated the capability of different versions of relatively well-explored machine learning (ML) models including random forest (RF), minimum probability machine regression (MPMR), M5 Tree (M5tree), extreme learning machine (ELM) and online sequential-ELM (OSELM) in predicting the most widely used DI known as standardized precipitation index (SPI) at multiple month horizons (i.e., 1, 3, 6 and 12). Models were developed using monthly rainfall data for the period of 1949–2013 at four meteorological stations namely, Barisal, Bogra, Faridpur and Mymensingh, each representing a geographical region of Bangladesh which frequently experiences droughts. The model inputs were decided based on correlation statistics and the prediction capability was evaluated using several statistical metrics including mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (R), Willmott’s Index of agreement (WI), Nash Sutcliffe efficiency (NSE), and Legates and McCabe Index (LM). The results revealed that the proposed models are reliable and robust in predicting droughts in the region. Comparison of the models revealed ELM as the best model in forecasting droughts with minimal RMSE in the range of 0.07–0.85, 0.08–0.76, 0.062–0.80 and 0.042–0.605 for Barisal, Bogra, Faridpur and Mymensingh, respectively for all the SPI scales except one-month SPI for which the RF showed the best performance with minimal RMSE of 0.57, 0.45, 0.59 and 0.42, respectively.
  •  
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